dsub
is a command-line tool that makes it easy to submit and run batch scripts
in the cloud.
The dsub
user experience is modeled after traditional high-performance
computing job schedulers like Grid Engine and Slurm. You write a script and
then submit it to a job scheduler from a shell prompt on your local machine.
Today dsub
supports Google Cloud as the backend batch job runner, along with a
local provider for development and testing. With help from the community, we'd
like to add other backends, such as a Grid Engine, Slurm, Amazon Batch,
and Azure Batch.
You can install dsub
from PyPI, or you can clone and
install from github.
Python 2 support ended in January 2020. Automated dsub
tests running on
Python 2 will soon be disabled.
See Python's official article for details.
Use Python 3.
This is optional, but whether installing from PyPI or from github, you are encouraged to use a Python virtual environment.
You can do this in a directory of your choosing.
python3 -m venv dsub_libs
source dsub_libs/bin/activate
Using a Python virtual environment isolates dsub
library dependencies from
other Python applications on your system.
Activate this virtual environment in any shell session before running dsub
.
To deactivate the virtual environment in your shell, run the command:
deactivate
Choose one of the following:
-
If necessary, install pip.
-
Install
dsub
pip install dsub
-
Be sure you have git installed
Instructions for your environment can be found on the git website.
-
Clone this repository.
git clone https://github.com/DataBiosphere/dsub cd dsub
-
Install dsub (this will also install the dependencies)
python setup.py install
-
Set up Bash tab completion (optional).
source bash_tab_complete
-
Minimally verify the installation by running:
dsub --help
-
(Optional) Install Docker.
This is necessary only if you're going to create your own Docker images or use the
local
provider.
We think you'll find the local
provider to be very helpful when building
your dsub
tasks. Instead of submitting a request to run your command on a
cloud VM, the local
provider runs your dsub
tasks on your local machine.
The local
provider is not designed for running at scale. It is designed
to emulate running on a cloud VM such that you can rapidly iterate.
You'll get quicker turnaround times and won't incur cloud charges using it.
-
Run a
dsub
job and wait for completion.Here is a very simple "Hello World" test:
dsub \ --provider local \ --logging /tmp/dsub-test/logging/ \ --output OUT=/tmp/dsub-test/output/out.txt \ --command 'echo "Hello World" > "${OUT}"' \ --wait
-
View the output file.
cat /tmp/dsub-test/output/out.txt
dsub
supports the use of two different APIs from Google Cloud for running
tasks. Google Cloud is transitioning from Genomics v2alpha1
to Cloud Life Sciences v2beta.
dsub
supports both APIs with the (old) google-v2
and (new) google-cls-v2
providers respectively. google-v2
is the current default provider. dsub
will be transitioning to make google-cls-v2
the default in coming releases.
The steps for getting started differ slightly as indicated in the steps below:
-
Sign up for a Google account and create a project.
-
Enable the APIs:
- For the
v2alpha1
API (provider:google-v2
):
Enable the Genomics, Storage, and Compute APIs.
- For the
v2beta
API (provider:google-cls-v2
):
- For the
-
Install the Google Cloud SDK and run
gcloud init
This will set up your default project and grant credentials to the Google Cloud SDK. Now provide credentials so
dsub
can call Google APIs:gcloud auth application-default login
-
Create a Google Cloud Storage bucket.
The dsub logs and output files will be written to a bucket. Create a bucket using the storage browser or run the command-line utility gsutil, included in the Cloud SDK.
gsutil mb gs://my-bucket
Change
my-bucket
to a unique name that follows the bucket-naming conventions.(By default, the bucket will be in the US, but you can change or refine the location setting with the
-l
option.) -
Run a very simple "Hello World"
dsub
job and wait for completion.-
For the
v2alpha1
API (provider:google-v2
):dsub
--provider google-v2
--project my-cloud-project
--regions us-central1
--logging gs://my-bucket/logging/
--output OUT=gs://my-bucket/output/out.txt
--command 'echo "Hello World" > "${OUT}"'
--wait
Change
my-cloud-project
to your Google Cloud project, andmy-bucket
to the bucket you created above.-
For the
v2beta
API (provider:google-cls-v2
):dsub
--provider google-cls-v2
--project my-cloud-project
--regions us-central1
--logging gs://my-bucket/logging/
--output OUT=gs://my-bucket/output/out.txt
--command 'echo "Hello World" > "${OUT}"'
--wait
Change
my-cloud-project
to your Google Cloud project, andmy-bucket
to the bucket you created above.The output of the script command will be written to the
OUT
file in Cloud Storage that you specify. -
-
View the output file.
gsutil cat gs://my-bucket/output/out.txt
Where possible, dsub
tries to support users being able to develop and test
locally (for faster iteration) and then progressing to running at scale.
To this end, dsub
provides multiple "backend providers", each of which
implements a consistent runtime environment. The current providers are:
- local
- google-v2 (the default)
- google-cls-v2 (new)
More details on the runtime environment implemented by the backend providers can be found in dsub backend providers.
The google-cls-v2
provider is built on the Cloud Life Sciences v2beta
API.
This API is very similar to its predecessor, the Genomics v2alpha1
API.
Details of the differences can be found in the
Migration Guide.
dsub
largely hides the differences between the two APIs, but there are a
few difference to note:
v2beta
is a regional service,v2alpha1
is a global service
What this means is that with v2alpha1
, the metadata about your tasks
(called "operations"), is stored in a global database, while with v2beta
, the
metadata about your tasks are stored in a regional database. If your operation
information needs to stay in a particular region, use the v2beta
API
(the google-cls-v2
provider), and specify the --location
where your
operation information should be stored.
- The
--regions
and--zones
flags can be omitted when usinggoogle-cls-v2
The --regions
and --zones
flags for dsub
specify where the tasks should
run. More specifically, this specifies what Compute Engine Zones to use for
the VMs that run your tasks.
With the google-v2
provider, there is no default region or zone, and thus
one of the --regions
or --zones
flags is required.
With google-cls-v2
, the --location
flag defaults to us-central1
, and
if the --regions
and --zones
flags are omitted, the location
will be
used as the default regions
list.
The following sections show how to run more complex jobs.
You can provide a shell command directly in the dsub command-line, as in the hello example above.
You can also save your script to a file, like hello.sh
. Then you can run:
dsub \
... \
--script hello.sh
If your script has dependencies that are not stored in your Docker image, you can transfer them to the local disk. See the instructions below for working with input and output files and folders.
By default, dsub uses a stock Ubuntu image. You can change the image
by passing the --image
flag.
dsub \
... \
--image ubuntu:16.04 \
--script hello.sh
Note: your --image
must include the
Bash shell interpreter.
You can pass environment variables to your script using the --env
flag.
dsub \
... \
--env MESSAGE=hello \
--command 'echo ${MESSAGE}'
The environment variable MESSAGE
will be assigned the value hello
when
your Docker container runs.
Your script or command can reference the variable like any other Linux
environment variable, as ${MESSAGE}
.
Be sure to enclose your command string in single quotes and not double
quotes. If you use double quotes, the command will be expanded in your local
shell before being passed to dsub. For more information on using the
--command
flag, see Scripts, Commands, and Docker
To set multiple environment variables, you can repeat the flag:
--env VAR1=value1 \
--env VAR2=value2
You can also set multiple variables, space-delimited, with a single flag:
--env VAR1=value1 VAR2=value2
dsub mimics the behavior of a shared file system using cloud storage bucket paths for input and output files and folders. You specify the cloud storage bucket path. Paths can be:
- file paths like
gs://my-bucket/my-file
- folder paths like
gs://my-bucket/my-folder
- wildcard paths like
gs://my-bucket/my-folder/*
See the inputs and outputs documentation for more details.
If your script expects to read local input files that are not already contained within your Docker image, the files must be available in Google Cloud Storage.
If your script has dependent files, you can make them available to your script by:
- Building a private Docker image with the dependent files and publishing the image to a public site, or privately to Google Container Registry
- Uploading the files to Google Cloud Storage
To upload the files to Google Cloud Storage, you can use the storage browser or gsutil. You can also run on data that’s public or shared with your service account, an email address that you can find in the Google Cloud Console.
To specify input and output files, use the --input
and --output
flags:
dsub \
... \
--input INPUT_FILE_1=gs://my-bucket/my-input-file-1 \
--input INPUT_FILE_2=gs://my-bucket/my-input-file-2 \
--output OUTPUT_FILE=gs://my-bucket/my-output-file \
--command 'cat "${INPUT_FILE_1}" "${INPUT_FILE_2}" > "${OUTPUT_FILE}"'
In this example:
- a file will be copied from
gs://my-bucket/my-input-file-1
to a path on the data disk - the path to the file on the data disk will be set in the environment variable
${INPUT_FILE_1}
- a file will be copied from
gs://my-bucket/my-input-file-2
to a path on the data disk - the path to the file on the data disk will be set in the environment variable
${INPUT_FILE_2}
The --command
can reference the file paths using the environment variables.
Also in this example:
- a path on the data disk will be set in the environment variable
${OUTPUT_FILE}
- the output file will written to the data disk at the location given by
${OUTPUT_FILE}
After the --command
completes, the output file will be copied to the bucket path gs://my-bucket/my-output-file
Multiple --input
, and --output
parameters can be specified and
they can be specified in any order.
To copy folders rather than files, use the --input-recursive
and
output-recursive
flags:
dsub \
... \
--input-recursive FOLDER=gs://my-bucket/my-folder \
--command 'find ${FOLDER} -name "foo*"'
Multiple --input-recursive
, and --output-recursive
parameters can be
specified and they can be specified in any order.
If you have one of the following:
- A large set of resource files, your code only reads a subset of those files, and the decision of which files to read is determined at runtime, or
- A large input file over which your code makes a single read pass or only needs to read a small range of bytes,
then you may find it more efficient at runtime to access this resource data via mounting a Google Cloud Storage bucket read-only or mounting a persistent disk created from a Compute Engine Image read-only.
The google-v2
and google-cls-v2
providers support these two methods of providing access to
resource data. The local
provider supports mounting a local directory in a
similar fashion to support your local development.
To have the google-v2
or google-cls-v2
provider mount a Cloud Storage bucket using
Cloud Storage FUSE, use the --mount
command line flag:
--mount MYBUCKET=gs://mybucket
The bucket will be mounted into the Docker container running your --script
or --command
and the location made available via the environment variable
${MYBUCKET}
. Inside your script, you can reference the mounted path using the
environment variable. Please read
Key differences from a POSIX file system
and Semantics
before using Cloud Storage FUSE.
To have the google-v2
or google-cls-v2
provider mount a persistent disk created from an image,
use the --mount
command line flag and the url of the source image and the size
(in GB) of the disk:
--mount MYDISK="https://www.googleapis.com/compute/v1/projects/your-project/global/images/your-image 50"
The image will be used to create a new persistent disk, which will be attached
to a Compute Engine VM. The disk will mounted into the Docker container running
your --script
or --command
and the location made available by the
environment variable ${MYDISK}
. Inside your script, you can reference the
mounted path using the environment variable.
To create an image, see Creating a custom image.
To have the local
provider mount a directory read-only, use the --mount
command line flag and a file://
prefix:
--mount LOCAL_MOUNT=file://path/to/my/dir
The local directory will be mounted into the Docker container running your
--script
or --command
and the location made available via the environment
variable ${LOCAL_MOUNT}
. Inside your script, you can reference the mounted
path using the environment variable.
dsub
tasks run using the local
provider will use the resources available on
your local machine.
dsub
tasks run using the google
, google-v2
, or google-cls-v2
providers can take advantage
of a wide range of CPU, RAM, disk, and hardware accelerator (eg. GPU) options.
See the Compute Resources documentation for details.
Each of the examples above has demonstrated submitting a single task with
a single set of variables, inputs, and outputs. If you have a batch of inputs
and you want to run the same operation over them, dsub
allows you
to create a batch job.
Instead of calling dsub
repeatedly, you can create
a tab-separated values (TSV) file containing the variables,
inputs, and outputs for each task, and then call dsub
once.
The result will be a single job-id
with multiple tasks. The tasks will
be scheduled and run independently, but can be
monitored and
deleted as a group.
The first line of the TSV file specifies the names and types of the parameters. For example:
--env SAMPLE_ID<tab>--input VCF_FILE<tab>--output OUTPUT_PATH
Each addition line in the file should provide the variable, input, and output values for each task. Each line beyond the header represents the values for a separate task.
Multiple --env
, --input
, and --output
parameters can be specified and
they can be specified in any order. For example:
--env SAMPLE<tab>--input A<tab>--input B<tab>--env REFNAME<tab>--output O
S1<tab>gs://path/A1.txt<tab>gs://path/B1.txt<tab>R1<tab>gs://path/O1.txt
S2<tab>gs://path/A2.txt<tab>gs://path/B2.txt<tab>R2<tab>gs://path/O2.txt
Pass the TSV file to dsub using the --tasks
parameter. This parameter
accepts both the file path and optionally a range of tasks to process.
The file may be read from the local filesystem (on the machine you're calling
dsub
from), or from a bucket in Google Cloud Storage (file name starts with
"gs://").
For example, suppose my-tasks.tsv
contains 101 lines: a one-line header and
100 lines of parameters for tasks to run. Then:
dsub ... --tasks ./my-tasks.tsv
will create a job with 100 tasks, while:
dsub ... --tasks ./my-tasks.tsv 1-10
will create a job with 10 tasks, one for each of lines 2 through 11.
The task range values can take any of the following forms:
m
indicates to submit taskm
(line m+1)m-
indicates to submit all tasks starting with taskm
m-n
indicates to submit all tasks fromm
ton
(inclusive).
The --logging
flag points to a location for dsub
task log files. For details
on how to specify your logging path, see Logging.
It's possible to wait for a job to complete before starting another. For details, see job control with dsub.
It is possible for dsub
to automatically retry failed tasks.
For details, see retries with dsub.
You can add custom labels to jobs and tasks, which allows you to monitor and cancel tasks using your own identifiers. In addition, with the Google providers, labeling a task will label associated compute resources such as virtual machines and disks.
For more details, see Checking Status and Troubleshooting Jobs
The dstat
command displays the status of jobs:
dstat --provider google-v2 --project my-cloud-project
With no additional arguments, dstat will display a list of running jobs for
the current USER
.
To display the status of a specific job, use the --jobs
flag:
dstat --provider google-v2 --project my-cloud-project --jobs job-id
For a batch job, the output will list all running tasks.
Each job submitted by dsub is given a set of metadata values that can be used for job identification and job control. The metadata associated with each job includes:
job-name
: defaults to the name of your script file or the first word of your script command; it can be explicitly set with the--name
parameter.user-id
: theUSER
environment variable value.job-id
: takes the formjob-name--userid--timestamp
where thejob-name
is truncated at 10 characters and thetimestamp
is of the formYYMMDD-HHMMSS-XX
, unique to hundredths of a second.task-id
: if the job is submitted with the--tasks
parameter, each task gets a sequential value of the form "task-n" where n is 1-based.
Note that the job metadata values will be modified to conform with the "Label Restrictions" listed in the Checking Status and Troubleshooting Jobs guide.
Metadata can be used to cancel a job or individual tasks within a batch job.
For more details, see Checking Status and Troubleshooting Jobs
By default, dstat outputs one line per task. If you're using a batch job with
many tasks then you may benefit from --summary
.
$ dstat --provider google-v2 --project my-project --status '*' --summary
Job Name Status Task Count
------------- ------------- -------------
my-job-name RUNNING 2
my-job-name SUCCESS 1
In this mode, dstat prints one line per (job name, task status) pair. You can see at a glance how many tasks are finished, how many are still running, and how many are failed/canceled.
The ddel
command will delete running jobs.
By default, only jobs submitted by the current user will be deleted.
Use the --users
flag to specify other users, or '*'
for all users.
To delete a running job:
ddel --provider google-v2 --project my-cloud-project --jobs job-id
If the job is a batch job, all running tasks will be deleted.
To delete specific tasks:
ddel \
--provider google-v2 \
--project my-cloud-project \
--jobs job-id \
--tasks task-id1 task-id2
To delete all running jobs for the current user:
ddel --provider google-v2 --project my-cloud-project --jobs '*'
-
See the examples:
-
See more documentation for: